Sign Language Interpreter Aptitude
03 - Signing/Interpreting Predictors Analysis
Freya Watkins
University of Birmingham30 March 2022
Setup
Load packages
library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
#library(plotrix) # for calculating standard error
library(scales) # scales on plots
library(lme4) # for linear mixed models
library(afex) # for linear mixed models
library(psych) # for factor analysisR / package versions used
R.Version() ## $platform
## [1] "x86_64-w64-mingw32"
##
## $arch
## [1] "x86_64"
##
## $os
## [1] "mingw32"
##
## $system
## [1] "x86_64, mingw32"
##
## $status
## [1] ""
##
## $major
## [1] "4"
##
## $minor
## [1] "0.5"
##
## $year
## [1] "2021"
##
## $month
## [1] "03"
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## $day
## [1] "31"
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## $`svn rev`
## [1] "80133"
##
## $language
## [1] "R"
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## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
##
## $nickname
## [1] "Shake and Throw"packageVersion('here')## [1] '1.0.1'packageVersion('tidyverse')## [1] '1.3.1'#packageVersion('plotrix')
packageVersion('scales') ## [1] '1.1.1'packageVersion('lme4') ## [1] '1.1.27.1'packageVersion('afex')## [1] '0.28.1'packageVersion('psych')## [1] '2.1.6'Read pre-wrangled data (see 00-wrangling-setup.Rmd script)
here::here()## [1] "C:/Users/freya/Desktop/PhD/3 SLI-Aptitude"apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)## # A tibble: 6 x 46
## session group ppt status selected continue L1_bsl prior_BSL age_s1
## <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 pre-degree pilot HW002 rejected 0 0 NA NA NA
## 2 pre-degree pilot HW004 rejected 0 0 NA NA NA
## 3 pre-degree pilot HW009 rejected 0 0 NA NA NA
## 4 pre-degree pilot HW011 rejected 0 0 NA NA NA
## 5 pre-degree pilot HW012 rejected 0 0 NA NA NA
## 6 pre-degree pilot HW014 rejected 0 0 NA NA NA
## # ... with 37 more variables: nback_lett <dbl>, nback_spat <dbl>,
## # nback_comb <dbl>, corsi_bspan <dbl>, corsi_score <dbl>, corsi_corr <dbl>,
## # corsi_mspan <dbl>, kirk_ceil <dbl>, kirk_raw <dbl>, kirk_acc <dbl>,
## # kbit_ceil <dbl>, kbit_raw <dbl>, kbit_acc <dbl>, dspan_mem <dbl>,
## # dspan_corr <dbl>, dspan_time <dbl>, mr2d_acc <dbl>, mr2d_rt <dbl>,
## # mr2d_sats <dbl>, mr3d_acc <dbl>, mr3d_rt <dbl>, mr3d_sats <dbl>,
## # bis_tot <dbl>, bis_att <dbl>, bis_mot <dbl>, bis_nplan <dbl>, ...Filter out participants who did not progress beyond interview
apt <- apt %>% filter(status != "rejected")Plots
Effects of predictors on outcome variables
Effects of predictors on BSL Grades
Dual N-Back vs BSL Grades
Letter Matching
# Create wider pivot table
nback_lett_grade_bsl_wide <- apt %>%
select(session, group, ppt, nback_lett, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_lett, grade_bsl))
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 1st year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 2nd year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 1st year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 2nd year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 2nd year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Spatial Matching
# Create wider pivot table
nback_spat_grade_bsl_wide <- apt %>%
select(session, group, ppt, nback_spat, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_spat, grade_bsl))
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 1st year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degre")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 2nd year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 1st year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 2nd year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 2nd year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Combined accuracy
# Create wider pivot table
nback_comb_grade_bsl_wide <- apt %>%
select(session, group, ppt, nback_comb, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_comb, grade_bsl))
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict BSL grades at the end of 1st year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict BSL grades at the end of 2nd year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 1st year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 2nd year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 2nd year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
###### Correlations
nback_comb_grade_bsl_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.170nback_comb_grade_bsl_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0798nback_comb_grade_bsl_wide %>%
filter(`nback_comb_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_1st year`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.397nback_comb_grade_bsl_wide %>%
filter(`nback_comb_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_1st year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.489nback_comb_grade_bsl_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.228Corsi Blocks vs BSL Grades
# Create wider pivot table
corsi_corr_grade_bsl_wide <- apt %>%
select(session, group, ppt, corsi_corr, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_corr, grade_bsl))
# Do Correct Responses on the Corsi Blocks Task pre-degree predict BSL grades at the end of 1st year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task pre-degree predict BSL grades at the end of 2nd year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 1st year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 2nd year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 2nd year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Kirklees Sentence Reading vs BSL Grades
# Create wider pivot table
kirk_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, kirk_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_acc, grade_bsl))
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 1st year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 2nd year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 1st year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 2nd year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 2nd year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Correlations
kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.433kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.108kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1st year`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.340kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1st year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.0910kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0908KBIT-2 Matrices vs BSL Grades
# Create wider pivot table
kbit_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, kbit_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_acc, grade_bsl))
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 1st year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 2nd year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 1st year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 2nd year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 2nd year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
###### Correlations
kbit_acc_grade_bsl_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`kbit_acc_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.118kbit_acc_grade_bsl_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kbit_acc_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0354kbit_acc_grade_bsl_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`kbit_acc_1st year`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.0442kbit_acc_grade_bsl_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kbit_acc_1st year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.175kbit_acc_grade_bsl_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kbit_acc_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.409Digit Span vs BSL Grades
Accuracy
# Create wider pivot table
dspan_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, dspan_corr, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_corr, grade_bsl))
# Does Accuracy on the Digit Span Task pre-degree predict BSL grades at the end of 1st year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task pre-degree predict BSL grades at the end of 2nd year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 19 rows containing non-finite values (stat_smooth).## Warning: Removed 19 rows containing missing values (geom_point).
# No datapoints for Digit Span @ '1 year of study'
# Does Accuracy on the Digit Span Task after 2 years of study predict BSL grades at the end of 2nd year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Memory Span
# Create wider pivot table
dspan_mem_grade_bsl_wide <- apt %>%
select(session, group, ppt, dspan_mem, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_mem, grade_bsl))
# Does Memory Span on the Digit Span Task pre-degree predict BSL grades at the end of 1st year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Memory Span on the Digit Span Task pre-degree predict BSL grades at the end of 2nd year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 19 rows containing non-finite values (stat_smooth).## Warning: Removed 19 rows containing missing values (geom_point).
# No datapoints for Digit Span @ 1 year of study session
# Does Memory Span on the Digit Span Task after 2 years of study predict BSL grades at the end of 2nd year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
2D Mental Rotation vs BSL Grades
Accuracy
# Create wider pivot table
mr2d_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr2d_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_acc, grade_bsl))
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 1st year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 2nd year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
###### Correlations
mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.128mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.136mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1st year`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.523mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1st year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.722mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.288RT
# Create wider pivot table
mr2d_rt_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr2d_rt, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_rt, grade_bsl))
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 1st year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 2nd year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.550# strongly driven by an outlier
mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.100# strongly driven by an outlier
mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1st year`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.473# strongly driven by an outlier
mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1st year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.219mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.3263D Mental Rotation vs BSL Grades
Accuracy
# Create wider pivot table
mr3d_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr3d_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_acc, grade_bsl))
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# only 2 datapoints for 3DMR @ timepoint 2
# Does Accuracy on the 3D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 3D Mental Rotation Task after 2 years") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
###### Correlations
mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.266mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.114mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.604RT
# Create wider pivot table
mr3d_rt_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr3d_rt, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_rt, grade_bsl))
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# only 2 datapoints for 3DMR @ timepoint 2
# Does Reaction Time on the 3D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Reaction Time on the 3D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task after 2 years of study") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
###### Correlations
mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_bsl_1st year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.136mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.164mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_2nd year`,
`grade_bsl_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.294Barratt Impulsiveness Scale vs BSL Grades
Total Impulsiveness
# Create wider pivot table
bis_tot_grade_bsl_wide <- apt %>%
select(session, group, ppt, bis_tot, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_tot, grade_bsl))
# Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study predict BSL grades at the end of 2nd year?
bis_tot_grade_bsl_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Total Impulsiveness on the Barratt Impulsiveness Scale\nafter 2 years of study predict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
Not sure why geom_smooth() line does not appear in knitted output (works in .Rmd file)
Effects of predictors on Interpreting Grades
Dual N-Back vs Interpreting Grades
Letter Matching
# Create wider pivot table
nback_lett_grade_terp_wide <- apt %>%
select(session, group, ppt, nback_lett, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_lett, grade_terp))
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 1st year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 2nd year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Spatial Matching
# Create wider pivot table
nback_spat_grade_terp_wide <- apt %>%
select(session, group, ppt, nback_spat, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_spat, grade_terp))
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 1st year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degre")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 2nd year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Combined accuracy
# Create wider pivot table
nback_comb_grade_terp_wide <- apt %>%
select(session, group, ppt, nback_comb, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_comb, grade_terp))
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 1st year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Combined Accuracy on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 2nd year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Combined Accuracy on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Combined Accuracy on the Dual N-Back Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Combined Accuracy on the Dual N-Back Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
###### Correlations
nback_comb_grade_terp_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_pre-degree`,
`grade_terp_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.111nback_comb_grade_terp_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.328nback_comb_grade_terp_wide %>%
filter(`nback_comb_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_1st year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.526nback_comb_grade_terp_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0265Corsi Blocks vs Interpreting Grades
# Create wider pivot table
corsi_corr_grade_terp_wide <- apt %>%
select(session, group, ppt, corsi_corr, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_corr, grade_terp))
# Do Correct Responses on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 1st year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 2nd year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Kirklees Sentence Reading vs Interpreting Grades
# Create wider pivot table
kirk_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, kirk_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_acc, grade_terp))
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 1st year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 2nd year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Correlations
kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_terp_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.400kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.305kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1st year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.225kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.214KBIT-2 Matrices vs Interpreting Grades
# Create wider pivot table
kbit_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, kbit_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_acc, grade_terp))
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 1st year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 2nd year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Digit Span vs Interpreting Grades
# Create wider pivot table
dspan_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, dspan_corr, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_corr, grade_terp))
# Does Accuracy on the Digit Span Task pre-degree predict Interpreting Grades at the end of 1st year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task pre-degree predict Interpreting Grades at the end of 2nd year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_pre-degree`,
`grade_terp_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.124dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.581dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.2662D Mental Rotation vs Interpreting Grades
Accuracy
# Create wider pivot table
mr2d_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, mr2d_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_acc, grade_terp))
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0795mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1st year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.614mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.288RT
# Create wider pivot table
mr2d_rt_grade_terp_wide <- apt %>%
select(session, group, ppt, mr2d_rt, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_rt, grade_terp))
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.239mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1st year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.107mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.1393D Mental Rotation vs Interpreting Grades
Accuracy
# Create wider pivot table
mr3d_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, mr3d_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_acc, grade_terp))
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 1st year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 17 rows containing non-finite values (stat_smooth).## Warning: Removed 17 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task after 2 years") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
Correlations
mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_terp_1st year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.127mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.221mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.552RT
# Create wider pivot table
mr3d_rt_grade_terp_wide <- apt %>%
select(session, group, ppt, mr3d_rt, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_rt, grade_terp))
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 1st year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 17 rows containing non-finite values (stat_smooth).## Warning: Removed 17 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Reaction Time on the 3D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task after 2 years of study") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
Correlations
mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_terp_1st year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.189mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.487mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_2nd year`,
`grade_terp_2nd year`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.184Barratt Impulsiveness Scale vs Interpreting Grades
Total Impulsiveness
# Create wider pivot table
bis_tot_grade_terp_wide <- apt %>%
select(session, group, ppt, bis_tot, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_tot, grade_terp))
# Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study predict Interpreting Grades at the end of 2nd year?
bis_tot_grade_terp_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_terp_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
Not sure why geom_smooth() line does not appear in knitted output (works in .Rmd file)
MODELS
What best predicts BSL grades?
#s4ppts <- apt %>% filter(ppt == "HW1041" | "HW1061" | "HW1121" | "HW1081" |
# "HW1111" | "HW1091" | "WV2011" | "WV2061" | "WV2081")
( bsl_grade_mdl <- lm(grade_bsl ~ nback_spat +
mr3d_sats +
corsi_corr +
kirk_acc +
kbit_acc +
dspan_corr,
data = apt) ) ##
## Call:
## lm(formula = grade_bsl ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + kbit_acc + dspan_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr kirk_acc kbit_acc
## 297.039 -266.145 6.222 -3.214 63.021 -39.777
## dspan_corr
## -36.297summary(bsl_grade_mdl)##
## Call:
## lm(formula = grade_bsl ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + kbit_acc + dspan_corr, data = apt)
##
## Residuals:
## 73 77 78 81 83 85 86 88
## 1.77554 0.93304 -2.02324 -0.31361 -0.87932 -0.09685 0.05466 0.84488
## 89 95
## 2.51948 -2.81457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 297.039 50.788 5.849 0.00996 **
## nback_spat -266.145 72.121 -3.690 0.03451 *
## mr3d_sats 6.222 1.128 5.514 0.01175 *
## corsi_corr -3.214 1.517 -2.119 0.12433
## kirk_acc 63.021 17.085 3.689 0.03455 *
## kbit_acc -39.777 45.853 -0.867 0.44950
## dspan_corr -36.297 15.243 -2.381 0.09752 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.827 on 3 degrees of freedom
## (122 observations deleted due to missingness)
## Multiple R-squared: 0.9643, Adjusted R-squared: 0.8929
## F-statistic: 13.5 on 6 and 3 DF, p-value: 0.02828# but is only being fit on 11 observations rn.....
( terp_grade_mdl <- lm(grade_terp ~ nback_spat +
mr3d_sats +
corsi_corr +
kirk_acc +
kbit_acc +
dspan_corr,
data = apt) )##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + kbit_acc + dspan_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr kirk_acc kbit_acc
## 298.804 -220.487 5.524 -5.475 82.897 -80.417
## dspan_corr
## -34.180summary(terp_grade_mdl)##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + kbit_acc + dspan_corr, data = apt)
##
## Residuals:
## 73 77 78 81 83 85 86 88 89 95
## 3.4474 0.5891 -2.3422 0.2106 -4.1558 -3.4497 1.7365 0.5756 4.5857 -1.1973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 298.804 88.449 3.378 0.0431 *
## nback_spat -220.487 125.602 -1.755 0.1774
## mr3d_sats 5.524 1.965 2.811 0.0672 .
## corsi_corr -5.475 2.641 -2.073 0.1299
## kirk_acc 82.897 29.755 2.786 0.0686 .
## kbit_acc -80.417 79.854 -1.007 0.3881
## dspan_corr -34.180 26.547 -1.288 0.2882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.924 on 3 degrees of freedom
## (122 observations deleted due to missingness)
## Multiple R-squared: 0.9128, Adjusted R-squared: 0.7384
## F-statistic: 5.233 on 6 and 3 DF, p-value: 0.1012# but is only being fit on 11 observations rn.....
# For SRT scores, model doesn't work because no grades yet at final session.
( srt_mdl <- lm(bsl_srt ~ nback_spat +
mr3d_sats +
corsi_corr +
kirk_acc +
kbit_acc +
dspan_corr,
data = apt) )##
## Call:
## lm(formula = bsl_srt ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + kbit_acc + dspan_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr kirk_acc kbit_acc
## -1050.33 1672.93 -35.97 -13.07 NA NA
## dspan_corr
## NAsummary(srt_mdl)##
## Call:
## lm(formula = bsl_srt ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + kbit_acc + dspan_corr, data = apt)
##
## Residuals:
## ALL 4 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1050.33 NA NA NA
## nback_spat 1672.93 NA NA NA
## mr3d_sats -35.97 NA NA NA
## corsi_corr -13.07 NA NA NA
## kirk_acc NA NA NA NA
## kbit_acc NA NA NA NA
## dspan_corr NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (128 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 3 and 0 DF, p-value: NAFactor Analysis
reduce tibble to just independent variables:
# apt <- select(apt, -session, -group, -ppt, -copy_score, -srt_score, -town_map, -bsl_grade)filter out rows with incomplete data
#apt_reduced <- filter(apt, corsi_corr != "na")
#apt_reduced <- filter(apt_reduced, nback_spat != "na")
#apt_reduced <- filter(apt_reduced, kirk_ceil != "na")reduce to just four tasks that were done at all 3 sessions
#apt_reduced <- select(apt_reduced, nback_lett:kbit_acc)FA using psych package
# fapsych <- fa(apt_reduced, nfactors=3, rotate="oblimin", scores="regression", residuals=FALSE, fm="minres")run factor analysis
# FA <- factanal(apt_reduced, factors=2, rotation="varimax", scores="regression")